Virtually defenseless: Moscow's attacks in Ukraine put fear into neighboring Moldova Palanca, Moldova - The village of Palanca felt the full horror of the war in neighboring Ukraine one December day. A mother was killed and her three children wounded by a Russian drone as they drove over the border bridge across the river Dniester into this previously quiet corner of southeastern Moldova, Ukrainian officials said. We are right across from there, and it terrified us, villager Maria Morari, 62, said of the two days of attacks on the crossing. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

Russia has intensified its strikes on the southern Ukrainian region of Odesa, causing widespread power cuts and threatening the region's maritime infrastructure. Ukrainian Deputy Prime Minister Oleksiy Kuleba said Moscow was carrying out systematic attacks on the region. Last week, he warned that the focus of the war may have shifted towards Odesa. President Volodymyr Zelensky said the repeated attacks were an attempt by Moscow to block Ukraine's access to maritime logistics. Earlier in December, Russian President Vladimir Putin threatened to sever Ukraine's access to the sea as retaliation for drone attacks on tankers of Russia's shadow fleet in the Black Sea.

What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? Here's where things stand on Sunday, November 30. A Russian drone attack killed one person and wounded 11, including a child, on the outskirts of the Ukrainian capital, Kyiv, regional Governor Mykola Kalashnyk said on Sunday.

Automatic speech recognition (ASR) has advanced in high-resource languages, but most of the world's 7,000+ languages remain unsupported, leaving thousands of long-tail languages behind. Expanding ASR coverage has been costly and limited by architectures that restrict language support, making extension inaccessible to most--all while entangled with ethical concerns when pursued without community collaboration. To transcend these limitations, we introduce Omnilingual ASR, the first large-scale ASR system designed for extensibility. Omnilingual ASR enables communities to introduce unserved languages with only a handful of data samples. It scales self-supervised pre-training to 7B parameters to learn robust speech representations and introduces an encoder-decoder architecture designed for zero-shot generalization, leveraging a LLM-inspired decoder. This capability is grounded in a massive and diverse training corpus; by combining breadth of coverage with linguistic variety, the model learns representations robust enough to adapt to unseen languages. Incorporating public resources with community-sourced recordings gathered through compensated local partnerships, Omnilingual ASR expands coverage to over 1,600 languages, the largest such effort to date--including over 500 never before served by ASR. Automatic evaluations show substantial gains over prior systems, especially in low-resource conditions, and strong generalization. We release Omnilingual ASR as a family of models, from 300M variants for low-power devices to 7B for maximum accuracy. We reflect on the ethical considerations shaping this design and conclude by discussing its societal impact. In particular, we highlight how open-sourcing models and tools can lower barriers for researchers and communities, inviting new forms of participation. Open-source artifacts are available at https://github.com/facebookresearch/omnilingual-asr.

Is the fall of Pokrovsk inevitable? Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? Russian forces launched 645 attacks on Ukraine's Zaporizhia region in the past day, killing one person in the Polohivskyi district, Governor Ivan Fedorov wrote in a post on Telegram. A Russian drone attack on a railway facility killed a security guard in Ukraine's Kherson region, Governor Oleksandr Prokudin wrote in a post on Facebook.

Emotions are a fundamental facet of human experience, varying across individuals, cultural contexts, and nationalities. Given the recent success of Large Language Models (LLMs) as role-playing agents, we examine whether LLMs exhibit emotional stereotypes when assigned nationality-specific personas. Specifically, we investigate how different countries are represented in pre-trained LLMs through emotion attributions and whether these attributions align with cultural norms. To provide a deeper interpretive lens, we incorporate four key cultural dimensions, namely Power Distance, Uncertainty Avoidance, Long-Term Orientation, and Individualism, derived from Hofstedes cross-cultural framework. Our analysis reveals significant nationality-based differences, with emotions such as shame, fear, and joy being disproportionately assigned across regions. Furthermore, we observe notable misalignment between LLM-generated and human emotional responses, particularly for negative emotions, highlighting the presence of reductive and potentially biased stereotypes in LLM outputs.

Criminal justice administrative data contain only a limited amount of information about the committed offense. However, there is an unused source of extensive information in continental European courts' decisions: descriptions of criminal behaviors in verdicts by which offenders are found guilty. In this paper, we study the feasibility of extracting these descriptions from publicly available court decisions from Slovakia. We use two different approaches for retrieval: regular expressions and large language models (LLMs). Our baseline was a simple method employing regular expressions to identify typical words occurring before and after the description. The advanced regular expression approach further focused on "sparing" and its normalization (insertion of spaces between individual letters), typical for delineating the description. The LLM approach involved prompting the Gemini Flash 2.0 model to extract the descriptions using predefined instructions. Although the baseline identified descriptions in only 40.5% of verdicts, both methods significantly outperformed it, achieving 97% with advanced regular expressions and 98.75% with LLMs, and 99.5% when combined. Evaluation by law students showed that both advanced methods matched human annotations in about 90% of cases, compared to just 34.5% for the baseline. LLMs fully matched human-labeled descriptions in 91.75% of instances, and a combination of advanced regular expressions with LLMs reached 92%.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? Russian and Ukrainian troops have fought battles in the ruins of Pokrovsk, a transport and logistics hub in eastern Ukraine, with Ukraine's military reporting fierce fighting under way in a part of the city that was key for Kyiv's front-line logistics. Ukrainian President Volodymyr Zelenskyy said he visited troops fighting near the eastern city of Dobropillia, where Ukrainian forces are conducting a counteroffensive against Russian troops. Russia struck civilian energy and port infrastructure in a massive overnight drone attack on Ukraine's southern region of Odesa, the region's governor said in a post on the Telegram messaging app, adding that rescuers extinguished fires and there were no casualties.

Measuring global AI diffusion remains challenging due to a lack of population-normalized, cross-country usage data. We introduce AI User Share, a novel indicator that estimates the share of each country's working-age population actively using AI tools. Built from anonymized Microsoft telemetry and adjusted for device access and mobile scaling, this metric spans 147 economies and provides consistent, real-time insight into global AI diffusion. We find wide variation in adoption, with a strong correlation between AI User Share and GDP. High uptake is concentrated in developed economies, though usage among internet-connected populations in lower-income countries reveals substantial latent demand. We also detect sharp increases in usage following major product launches, such as DeepSeek in early 2025. While the metric's reliance solely on Microsoft telemetry introduces potential biases related to this user base, it offers an important new lens into how AI is spreading globally. AI User Share enables timely benchmarking that can inform data-driven AI policy.